J
Javier Calpe-Maravilla
Researcher at University of Valencia
Publications - 43
Citations - 2332
Javier Calpe-Maravilla is an academic researcher from University of Valencia. The author has contributed to research in topics: Support vector machine & Contextual image classification. The author has an hindex of 16, co-authored 42 publications receiving 2155 citations.
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Composite kernels for hyperspectral image classification
Gustau Camps-Valls,Luis Gómez-Chova,Jordi Muñoz-Marí,Joan Vila-Francés,Javier Calpe-Maravilla +4 more
TL;DR: This framework of composite kernels demonstrates enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only, flexibility to balance between the spatial and spectral information in the classifier, and computational efficiency.
Journal ArticleDOI
Robust support vector method for hyperspectral data classification and knowledge discovery
Gustau Camps-Valls,Luis Gómez-Chova,Javier Calpe-Maravilla,José D. Martín-Guerrero,Emilio Soria-Olivas,L. Alonso-Chorda,Jose Moreno +6 more
TL;DR: Support vector machines yield better outcomes than neural networks regarding accuracy, simplicity, and robustness, and training neural and neurofuzzy models is unfeasible when working with high-dimensional input spaces and great amounts of training data.
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Cloud-Screening Algorithm for ENVISAT/MERIS Multispectral Images
TL;DR: The proposed modular methodology constitutes a general framework that can be applied to multispectral images acquired by spaceborne sensors working in the visible and near-infrared spectral range with proper spectral information to characterize atmospheric-oxygen and water-vapor absorptions.
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Mean Map Kernel Methods for Semisupervised Cloud Classification
TL;DR: A semisupervised support vector machine classifier based on the combination of clustering and the mean map kernel is proposed, which reinforces samples in the same cluster belonging to the same class by combining sample and cluster similarities implicitly in the kernel space.
Journal ArticleDOI
Retrieval of oceanic chlorophyll concentration with relevance vector machines
Gustavo Camps-Valls,Luis Gómez-Chova,Jordi Muñoz-Marí,Joan Vila-Francés,Julia Amorós-López,Javier Calpe-Maravilla +5 more
TL;DR: Results suggest that RVMs offer an excellent trade-off between accuracy and sparsity of the solution, and become less sensitive to the selection of the free parameters.